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Apache Flink VS TimescaleDB

Compare Apache Flink VS TimescaleDB and see what are their differences

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Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

TimescaleDB logo TimescaleDB

TimescaleDB is a time-series SQL database providing fast analytics, scalability, with automated data management on a proven storage engine.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • TimescaleDB Landing page
    Landing page //
    2023-09-23

Apache Flink features and specs

  • Real-time Stream Processing
    Apache Flink is designed for real-time data streaming, offering low-latency processing capabilities that are essential for applications requiring immediate data insights.
  • Event Time Processing
    Flink supports event time processing, which allows it to handle out-of-order events effectively and provide accurate results based on the time events actually occurred rather than when they were processed.
  • State Management
    Flink provides robust state management features, making it easier to maintain and query state across distributed nodes, which is crucial for managing long-running applications.
  • Fault Tolerance
    The framework includes built-in mechanisms for fault tolerance, such as consistent checkpoints and savepoints, ensuring high reliability and data consistency even in the case of failures.
  • Scalability
    Apache Flink is highly scalable, capable of handling both batch and stream processing workloads across a distributed cluster, making it suitable for large-scale data processing tasks.
  • Rich Ecosystem
    Flink has a rich set of APIs and integrations with other big data tools, such as Apache Kafka, Apache Hadoop, and Apache Cassandra, enhancing its versatility and ease of integration into existing data pipelines.

Possible disadvantages of Apache Flink

  • Complexity
    Flink’s advanced features and capabilities come with a steep learning curve, making it more challenging to set up and use compared to simpler stream processing frameworks.
  • Resource Intensive
    The framework can be resource-intensive, requiring substantial memory and CPU resources for optimal performance, which might be a concern for smaller setups or cost-sensitive environments.
  • Community Support
    While growing, the community around Apache Flink is not as large or mature as some other big data frameworks like Apache Spark, potentially limiting the availability of community-contributed resources and support.
  • Ecosystem Maturity
    Despite its integrations, the Flink ecosystem is still maturing, and certain tools and plugins may not be as developed or stable as those available for more established frameworks.
  • Operational Overhead
    Running and maintaining a Flink cluster can involve significant operational overhead, including monitoring, scaling, and troubleshooting, which might require a dedicated team or additional expertise.

TimescaleDB features and specs

  • Scalability
    TimescaleDB offers excellent horizontal and vertical scalability, which allows it to handle large volumes of data efficiently. Its architecture is designed to accommodate growth by distributing and efficiently managing data shards.
  • Time-Series Data Optimization
    Specifically optimized for time-series data, TimescaleDB provides features like hypertables and continuous aggregates that speed up queries and optimize storage for time-based data.
  • SQL Compatibility
    As an extension of PostgreSQL, TimescaleDB offers full SQL support, making it familiar to developers and allowing easy integration with existing SQL-based systems and applications.
  • Retention Policies
    TimescaleDB includes built-in data retention policies, enabling automatic management of historical data and freeing up storage by performing automatic data roll-ups or deletes.
  • Integration with the PostgreSQL Ecosystem
    It benefits from PostgreSQL's rich ecosystem of extensions, tools, and optimizations, allowing for versatile use cases beyond just time-series data while maintaining robust reliability and performance.

Possible disadvantages of TimescaleDB

  • Learning Curve
    Although it’s SQL-based, developers might face a learning curve to fully leverage TimescaleDB's time-series specific features such as hypertables and specific optimization techniques.
  • Limited Write Scalability
    While it's scalable, TimescaleDB might face challenges with extremely high-throughput write workloads compared to some NoSQL time-series databases, which are specifically built for such tasks.
  • Dependency on PostgreSQL
    As it operates as a PostgreSQL extension, any limitations and issues in PostgreSQL might directly affect TimescaleDB's performance and capabilities.
  • Complexity in Setup for High Availability
    Setting up TimescaleDB with high availability and distributed systems might introduce complexities, particularly for organizations that are not well-versed in PostgreSQL clustering and replication strategies.
  • Storage Overhead
    The additional storage features add an overhead, which means that while it adds value with its optimizations, users need to manage storage resources effectively, especially in environments with very large datasets.

Apache Flink videos

+ Add

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

TimescaleDB videos

+ Add

Rearchitecting a SQL Database for Time-Series Data | TimescaleDB

More videos:

  • Review - Visualizing Time-Series Data with TimescaleDB and Grafana

Category Popularity

0-100% (relative to Apache Flink and TimescaleDB)

Apache Flink

TimescaleDB

Big Data
100 100%
Big Data
0% 0
Databases
43 43%
Databases
57% 57
Stream Processing
100 100%
Stream Processing
0% 0
Time Series Database
0 0%
Time Series Database
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Flink and TimescaleDB

Apache Flink Reviews

We have no reviews of Apache Flink yet.
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TimescaleDB Reviews

ClickHouse vs TimescaleDB
Recently, TimescaleDB published a blog comparing ClickHouse & TimescaleDB using timescale/tsbs, a timeseries benchmarking framework. I have some experience with PostgreSQL and ClickHouse but never got the chance to play with TimescaleDB. Some of the claims about TimescaleDB made in their post are very bold, that made me even more curious. I thought it’d be a great...
Source: pradeepchhetri.xyz
4 Best Time Series Databases To Watch in 2019
The Guardian did a very nice article explaining on they went from MongoDB to PostgresSQL in the favor of scaling their architecture and encrypting their content at REST. As you can tell, big companies are relying on SQL-constraint systems (with a cloud architecture of course) to ensure system reliability and accessibility. I believe that PostgresSQL will continue to grow, so...
Source: medium.com
20+ MongoDB Alternatives You Should Know About
TimescaleDB If on the other hand you are storing time series data in MongoDB, then TimescaleDB might be a good fit.
Source: www.percona.com

Social recommendations and mentions

Based on our record, Apache Flink should be more popular than TimescaleDB. It has been mentiond 40 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Flink mentions (40)

  • Is RisingWave the Next Apache Flink?
    Apache Flink, known initially as Stratosphere, is a distributed stream processing engine initiated by a group of researchers at TU Berlin. Since its initial release in May 2011, Flink has gained immense popularity in both academia and industry. And it is currently the most well-known streaming system globally (challenge me if you think I got it wrong!). - Source: dev.to / 7 days ago
  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 12 days ago
  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / 17 days ago
  • Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored
    Apache Flink: Flink is a unified streaming and batching platform developed under the Apache Foundation. It provides support for Java API and a SQL interface. Flink boasts a large ecosystem and can seamlessly integrate with various services, including Kafka, Pulsar, HDFS, Iceberg, Hudi, and other systems. - Source: dev.to / 25 days ago
  • Exploring the Power and Community Behind Apache Flink
    In conclusion, Apache Flink is more than a big data processing tool—it is a thriving ecosystem that exemplifies the power of open source collaboration. From its impressive technical capabilities to its innovative funding model, Apache Flink shows that sustainable software development is possible when community, corporate support, and transparency converge. As industries continue to demand efficient real-time data... - Source: dev.to / about 2 months ago
View more

TimescaleDB mentions (5)

  • Ask HN: Does anyone use InfluxDB? Or should we switch?
    (:alert: I work for Timescale :alert:) It's funny, we hear this more and more "we did some research and landed on Influx and ... Help it's confusing". We actually wrote an article about what we think, you can find it here: https://www.timescale.com/blog/what-influxdb-got-wrong/ As the QuestDB folks mentioned if you want a drop in replacement for Influx then they would be an option, it kinda sounds that's not what... - Source: Hacker News / over 1 year ago
  • Best small scale dB for time series data?
    If you like PostgreSQL, I'd recommend starting with that. Additionally, you can try TimescaleDB (it's a PostgreSQL extension for time-series data with full SQL support) it has many features that are useful even on a small-scale, things like:. Source: over 2 years ago
  • Quick n Dirty IoT sensor & event storage (Django backend)
    I have built a Django server which serves up the JSON configuration, and I'd also like the server to store and render sensor graphs & event data for my Thing. In future, I'd probably use something like timescale.com as it is a database suited for this application. However right now I only have a handful of devices, and don't want to spend a lot of time configuring my back end when the Thing is my focus. So I'm... Source: over 3 years ago
  • How fast and scalable is TimescaleDB compare to a NoSQL Database?
    I've seen a lot of benchmark results on timescale on the web but they all come from timescale.com so I just want to ask if those are accurate. Source: over 3 years ago
  • The State of PostgreSQL 2021 Survey is now open!
    Ryan from Timescale here. We (TimescaleDB) just launched the second annual State of PostgreSQL survey, which asks developers across the globe about themselves, how they use PostgreSQL, their experiences with the community, and more. Source: about 4 years ago

What are some alternatives?

When comparing Apache Flink and TimescaleDB, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

InfluxData - Scalable datastore for metrics, events, and real-time analytics.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Prometheus - An open-source systems monitoring and alerting toolkit.

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

VictoriaMetrics - Fast, easy-to-use, and cost-effective time series database

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